Compound selection for development – Is salt formation the ultimate answer? Experiences with an extended concept of the “100mg approach”

Korn, Christian; Balbach, Stefan

doi:10.1016/j.ejps.2013.08.040

Cited by 44 publications

(24 citation statements)

References 23 publications

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“…The most popular agglomerative hierarchal clustering method that has a wide range of applications in drug discovery process is ward clustering algorithm [5][6][7][8][9][10][11][12]. Ward clustering methods implemented using stored-matrix algorithm, this matrix contains all pairwise proximities between compounds in the data set to be clustered.…”

Section: Analyze the Resultsmentioning

confidence: 99%

“…Many clustering algorithms are available for Chemoinformatics but the most popular clustering algorithm is ward clustering algorithm. This algorithm used in many applications in drug discovery process such as compound selection, compound acquisition, High-Throughput Screening (HTS), Quantitative Structure-Activity Relationship (QSAR) analysis and Absorption, Distribution, Metabolism, Elimination, Toxicity (ADMET) prediction [5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Parallel Ward Algorithm for Drug Discovery

Malhat

Mousa

2015

IJCI. International Journal of Computers and Information

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Millions of compounds are now available in chemical libraries and scientists have to test these compounds against biological targets in order to identify lead compounds. The identification of lead compounds is a key step in the drug discovery process. So, there are many hierarchical clustering algorithms are developed and modified for that purpose. Ward algorithm is one of the most popular hierarchical clustering algorithms that are used in many applications in the drug discovery process because of it is accuracy. But, it has limitation to handle large data sets within a reasonable time and memory resources. In this paper, we evaluate and compare two parallel approaches to run ward algorithm. The two approaches are parallel for loop and MapReduce framework. The results shows that parallel for loop failed to reduce computational time of ward algorithm due to overhead needed for data communications. But, MapReduce framework shows considerable reduction in computational time. The parallel ward algorithm saves 17% of time using three nodes and saves 58% of time using six nodes using MapReduce.

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Section: Analyze the Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating Parallel Ward Algorithm for Drug Discovery

Malhat

Mousa

2015

IJCI. International Journal of Computers and Information

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“…At present, most widely used drugs, natural products, and drug candidates are insoluble in water, and increasing the solubility of poorly soluble drugs is an important problem for many drug molecules [1][2][3]. Several strategies have been studied to overcome this problem, such as salt formation [4][5][6], nanoparticles [7][8][9][10], inclusion complexes [11], liposomes [12,13], and amorphous solid dispersions [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Mechanism and Improved Dissolution of Glycyrrhetinic Acid Solid Dispersion by Alkalizers

et al. 2020

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The purpose of this study was to increase the dissolution of glycyrrhetinic acid (GA) by preparing ternary solid dispersion (TSD) systems containing alkalizers, and to explore the modulating mechanism of alkalizers in solid dispersion systems. GA TSDs were prepared by hot melt extrusion (HME) with Kollidon® VA64 as the carrier and L-arginine/meglumine as the alkalizers. The in vitro release of the TSD was investigated with a dissolution test, and the dissociation constant (pKa) was used to describe the ionization degree of the drug in different pH buffers. Scanning electron microscopy (SEM), differential scanning calorimetry (DSC), X-ray powder diffraction (XRPD), Fourier Transform Infrared Spectroscopy (FTIR), Raman spectra, X-ray photoelectron spectroscopy (XPS), and a molecular model were used for solid-state characterizations and to study the dissolution mechanism of the TSDs. It was evident that the dissolution of GA significantly increased as a result of the TSD compared to the pure drug and binary solid dispersion. SEM, DSC, and XPRD data showed that GA transformed into an amorphous form in TSD. As illustrated by FTIR, Raman, XPS, and molecular docking, high binding energy ion-pair complexes formed between GA and the alkalizers during the process of HME. These can destroy the H-bond between GA molecules. Further, intermolecular H-bonds formed between the alkalizers and Kollidon® VA64, which can increase the wettability of the drug. Our results will significantly improve the solubility and dissolution of GA. In addition, the lower pKa value of TSD indicates that higher ionization is beneficial to the dissolution of the drug. This study should facilitate further developments of TSDs containing alkalizers to improve the dissolution of weakly acidic drugs and gain a richer understanding of the mechanism of dissolution.

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“…Many clustering algorithms are available for chemoinformatics but the most popular clustering algorithm is ward clustering algorithm. This algorithm used in many applications in drug discovery process such as compound selection, compound acquisition, High-Throughput Screening (HTS), Quantitative Structure-Activity Relationship (QSAR) analysis and Absorption, Distribution, Metabolism, Elimination, Toxicity (ADMET) prediction [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Parallel ward clustering for chemical compounds using OpenCL

Malhat¹,

El-Sisi²

2015

2015 Tenth International Conference on Computer Engineering &Amp; Systems (ICCES)

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The availability of chemical libraries with millions of compounds makes the process of identifying lead compounds very hard. The identification of these compounds is the backbone step of drug discovery process. Hierarchical clustering algorithms are used for that purpose. One of the most popular hierarchical clustering algorithms that are used in many applications in the drug discovery process is ward clustering algorithm. A main problem with the previous implementations of ward algorithm is its limitation to handle large data sets within a reasonable time and memory resources. In this paper, OpenCL is used to implement ward algorithm. The first two steps of ward (1) proximity matrix computation; (2) finding minimum distance are modified to run in parallel. Four subsets of National Cancer Institute (NCI) dataset are used. The smallest subset contains 500 compounds and largest subset contains 10,000 compounds. The results show that parallel proximity matrix computation saves 92% of time for smallest subset and 99% of time for largest subset. The parallel minimum distance saves 76% of time for smallest subset and 99% of time for largest subset.

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Compound selection for development – Is salt formation the ultimate answer? Experiences with an extended concept of the “100mg approach”

Cited by 44 publications

References 23 publications

Evaluating Parallel Ward Algorithm for Drug Discovery

Evaluating Parallel Ward Algorithm for Drug Discovery

Mechanism and Improved Dissolution of Glycyrrhetinic Acid Solid Dispersion by Alkalizers

Parallel ward clustering for chemical compounds using OpenCL

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